Prognostic model of small sample critical diseases based on transfer learning.
10.7507/1001-5515.201905074
- Author:
Jing XIA
1
;
Su PAN
1
;
Molei YAN
2
;
Guolong CAI
2
;
Jing YAN
2
;
Gangmin NING
1
Author Information
1. College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, P.R.China.
2. Department of ICU, Zhejiang Hospital, Hangzhou 310013, P.R.China.
- Publication Type:Journal Article
- Keywords:
critical disease;
long short-term memory;
prognostic model;
small samples;
transfer learning
- From:
Journal of Biomedical Engineering
2020;37(1):1-9
- CountryChina
- Language:Chinese
-
Abstract:
Aiming at the problem that the small samples of critical disease in clinic may lead to prognostic models with poor performance of overfitting, large prediction error and instability, the long short-term memory transferring algorithm (transLSTM) was proposed. Based on the idea of transfer learning, the algorithm leverages the correlation between diseases to transfer information of different disease prognostic models, constructs the effictive model of target disease of small samples with the aid of large data of related diseases, hence improves the prediction performance and reduces the requirement for target training sample quantity. The transLSTM algorithm firstly uses the related disease samples to pretrain partial model parameters, and then further adjusts the whole network with the target training samples. The testing results on MIMIC-Ⅲ database showed that compared with traditional LSTM classification algorithm, the transLSTM algorithm had 0.02-0.07 higher AUROC and 0.05-0.14 larger AUPRC, while its number of training iterations was only 39%-64% of the traditional algorithm. The results of application on sepsis revealed that the transLSTM model of only 100 training samples had comparable mortality prediction performance to the traditional model of 250 training samples. In small sample situations, the transLSTM algorithm has significant advantages with higher prediciton accuracy and faster training speed. It realizes the application of transfer learning in the prognostic model of critical disease with small samples.
